Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers
Authors: Buyun He, Yingguang Yang, Qi Wu, Hao Liu, Renyu Yang, Hao Peng, Xiang Wang, Yong Liao, Pengyuan Zhou
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superiority of Bot DGT against the leading methods that neglected the dynamic nature of social networks in terms of accuracy, recall, and F1-score. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China 2Beihang University 3Harbin Engineering University 4Aarhus university |
| Pseudocode | No | The paper describes its methodology in prose and mathematical equations but does not include pseudocode or an algorithm block. |
| Open Source Code | Yes | Our code is publicly available on Git Hub1. 1https://github.com/Peien429/Bot DGT |
| Open Datasets | Yes | We conduct experiments on two comprehensive social bot detection benchmarks: Twi Bot-20 [Feng et al., 2021a] and Twi Bot-22 [Feng et al., 2022b]. |
| Dataset Splits | No | The paper mentions using Twi Bot-20 and Twi Bot-22 datasets but does not explicitly detail the training, validation, and test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not specify the version numbers of any software dependencies or libraries (e.g., Python, PyTorch, TensorFlow, or specific library versions) used for implementation or experimentation. |
| Experiment Setup | No | The paper describes the model architecture and general experimental setup, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |